![]() In our case, we are dealing with lesion images, and it becomes all the more necessary to be able to interpret the model.One way of accomplishing this is through trainable attention mechanisms (but you already know this, right? Read on.) When training an image model, we want the model to be able to focus on important parts of the image.If you are still not convinced on why should we use attention with images, here are a few more points to make the case: We'll be classifying melanoma images as malignant and benign, and later try to interpret the predictions.īut we could classify images without attention, right? In this blog, we'll use attention for a noble pursuit. Now, we have a pretty good picture of what attention does. While the pixel in the gray boxes would be weighed less. The attention mechanism weighs the pixel in the correct boxes more w.r.t the pixel in the yellow box. However, by just looking at the pixels in the gray boxes, you won't be able to predict what should come in the yellow box. If we focus at the features of the dog in the red boxes, like his nose, right pointy ear and mystery eyes, we'll be able to guess what should come in the yellow box. Let us look at an example mentioned in this excellent blog, Attention? Attention!. When we predict an element, which could be a pixel in an image or a word in a sentence, we use the attention vector to infer how much is it related to the other elements. In deep learning, attention can be interpreted as a vector of importance weights. Since working memory in both humans and machines is limited, this process is key to not overwhelming a system's memory. The main aim of this function is to emphasize the important parts of the information, and try to de-emphasize the non-relevant parts. The attention mechanism in Neural Networks tends to mimic the cognitive attention possessed by human beings. While attention fundamentally bought a huge shift in NLP, it is still not widely used with Computer Vision problems.īefore we move forward, let us try to understand attention a bit better. Meanwhile, the field of NLP was also growing rapidly, and following the release of the seminal paper Attention is all you need!, it has never been the same. Then came Resnet, VGG-16 and a lot of other algorithms that continued to improved upon Imagenet and Alexnet. Then, Google’s Inception networks introduced a new concept of stacking convolutions with different filter sizes which take in the same input, and managed to outdo Alexnet on the Imagenet challenge. As a field, image classification became famous after the first ImageNet challenge because of the the novel availability of the huge ImageNet dataset, the success of Deep Neural Networks and the corresponding, astonishing performance it received on the dataset. The process of image classification involves comprehending the contextual information in images to classify them into a set of predefined labels. Image Classification is perhaps one of the most popular subdomains in Computer Vision.
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